Controle por Modos Deslizantes de um Atuador Eletro-hidráulico com Compensação por Processo Gaussiano / Sliding Mode Control of an Electric-Hydraulic Actuator with Gaussian Process Compensation

Gabriel da Silva Lima, Wallace Moreira Bessa

Resumo


O desenvolvimento de sistemas de controle precisos para atuadores eletro-hidráulicos depende de uma adequada compensação dos efeitos dinâmicos desconhecidos. Neste trabalho, um controlador por Modos Deslizantes é combinado com um compensador por Processo Gaussiano para proporcionar um adequado rastreamento de trajetória. Processo Gaussiano é uma conhecida estratégia de aprendizagem de máquinas que pode ser utilizada no reconhecimento de funções. As propriedades de convergência do sistema em malha fechada são analisadas pela Teoria de Estabilidade de Lyapunov. Resultados numéricos confirmam uma forte melhora no desempenho do controlador ao ser inserido o compensador proposto.


Palavras-chave


Sistemas de controle; Atuadores eletro-hidráulicos; Modos Deslizantes; Processo Gaussiano; Aprendizagem de máquina

Texto completo:

PDF

Referências


ALTARE, G. and VACCA, A. A design solution for efficient and compact electro-hydraulic actuators. Procedia Engineering, 106, 8-16. 2015.

ARAN, V. and UNEL, M. Gaussian process regression feedforward controller for diesel engine airpath. International Journal of Automotive Technology, 19(1), 635-642. 2018.

BALAU, A.E., CARUNTU, C.F., and LAZAR, C. Simulation and control of an electro-hydraulic actuated clutch. Mechanical Systems and Signal Processing, 25(6), 1911-1922. 2011.

BESSA, W.M., DUTRA, M.S., and KREUZER, E. Sliding mode control with adaptive fuzzy dead-zone compensation of an electro-hydraulic servo-system. Journal of Intelligent and Robotic Systems, 58(1), 3-16. 2010.

BESSA, W.M. Some remarks on the boundedness and convergence properties of smooth sliding mode controllers. International Journal of Automation and Computing, 6(2), 154-158. 2009.

BONCHIS, A., CORKE, P.I., and RYE, D.C. Experimental evaluation of position control methods for hydraulic systems. IEEE Transactions on Control Systems Technology, 10(6), 876-882. 2002.

DOERR, A., DANIEL, C., NGUYEN-TUONG, D., MARCO, A., SCHAAL, S., MARC, T., and TRIMPE, S. Optimizing long-term predictions for model-based policy search. In Conference on Robot Learning, 227-238. 2017.

DOERR, A., DANIEL, C., SCHIEGG, M., NGUYEN-TUONG, D., SCHAAL, S., TOUSSAINT, M., and TRIMPE, S. Probabilistic Recurrent State-Space Models. In Proceedings of the 35th International Conference on Machine Learning, 1280-1289. Stockholm. 2018.

LANG, M. and HIRCHE, S. Computationally efficient rigid-body gaussian process for motion dynamics. IEEE Robotics and Automation Letters, 2(3), 1601-1608. 2017.

LEVANT, A. Higher-order sliding modes, differentiation and output-feedback control. International journal of Control, 76(9-10), 924-941. 2003.

LIEM, D.T., TRUONG, D.Q., PARK, H.G., and AHN, K.K. A feedforward neural network fuzzy grey predictor-based controller for force control of an electrohydraulic actuator. International Journal of Precision Engineering and Manufacturing, 17(3), 309-321. 2016.

LIMA, E.L. Curso de análise, Volume 1. Projeto Euclides, IMPA, 11th edition. 2004.

LIMA, G.S., BESSA, W.M., and TRIMPE, S. Depth control of underwater robots using sliding modes and gaussian process regression. In IEEE LARS 2018 -15th Latin American Robotics Symposium. IEEE, João Pessoa. 2018.

LIU, R. and ALLEYNE, A. Nonlinear force/pressure tracking of an electro-hydraulic actuator. IFAC Proceedings Volumes, 32(2), 952-957. 1999.

PISANO, A. and USAI, E. Output-feedback control of an underwater vehicle prototype by higher-order sliding modes. Automatica, 40(9), 1525-1531. 2004.

SANTOS, J.D.B. Compensação de atrito no controle de sistemas mecânicos : Uma abordagem utilizando estratégias inteligentes. Ph.D. thesis, Universidade Federal do Rio Grande do Norte, Natal. 2018.

SKARPETIS, M.G. and KOUMBOULIS, F.N. Robust pid controller for electro|hydraulic actuators. In Emerging Technologies & Factory Automation (ETFA), 2013 IEEE 18th Conference on, 1-5. IEEE. 2013.

SLOTINE, J.J.E. and LI, W. Applied Nonlinear Control. Prentice-Hall, Inc., Englewood Cliffs, 1 edition. 1991.

SUN, H. and CHIU, G.C. Nonlinear observer based force control of electro-hydraulic actuators. In American Control Conference, 1999. Proceedings of the 1999, volume 2, 764-768. IEEE. 1999.

WILLIAMS, C.K. and RASMUSSEN, C.E. Gaussian processes for machine learning. the MIT Press, 2(3), 4. 2006.

XILOYANNIS, M., GAVRIEL, C., THOMIK, A.A., and FAISAL, A.A. Gaussian Process Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control with Natural Hand Kinematics. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1785-1801. 2017.

YANG, Y., HUANG, D., and DONG, X. Robust repetitive learning control of lower limb exoskeleton with hybrid electro-hydraulic system. In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), 718-723. IEEE. 2018.

YIM, J., KIM, S., and CHOI, Y. Adaptive torque control of hydraulic actuators based on dynamic compensation. In Ubiquitous Robots and Ambient Intelligence (URAI), 2014 11th International Conference on, 448-451. IEEE. 2014.




DOI: https://doi.org/10.34115/basrv4n3-061

Apontamentos

  • Não há apontamentos.